Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression
This paper proposes a biomass concentration estimator for a batch biotechnological process based on Bayesian regression with Gaussian process. On the basis of experimental data, a two-stage bootstrap technique has been developed for the estimator design. In the first stage, the biomass data set was...
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| Published in: | Computers & chemical engineering Vol. 32; no. 12; pp. 3264 - 3273 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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22-12-2008
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| Abstract | This paper proposes a biomass concentration estimator for a batch biotechnological process based on Bayesian regression with Gaussian process. On the basis of experimental data, a two-stage bootstrap technique has been developed for the estimator design. In the first stage, the biomass data set was augmented with virtual filtered measurements, and in the second stage, the biomass estimator design was completed. The method provides information on the confidence level of the estimates, and the biomass estimator performances are illustrated for the
Bacillus thuringiensis δ-endotoxins production process. |
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| AbstractList | This paper proposes a biomass concentration estimator for a batch biotechnological process based on Bayesian regression with Gaussian process. On the basis of experimental data, a two-stage bootstrap technique has been developed for the estimator design. In the first stage, the biomass data set was augmented with virtual filtered measurements, and in the second stage, the biomass estimator design was completed. The method provides information on the confidence level of the estimates, and the biomass estimator performances are illustrated for the
Bacillus thuringiensis δ-endotoxins production process. This paper proposes a biomass concentration estimator for a batch biotechnological process based on Bayesian regression with Gaussian process. On the basis of experimental data, a two-stage bootstrap technique has been developed for the estimator design. In the first stage, the biomass data set was augmented with virtual filtered measurements, and in the second stage, the biomass estimator design was completed. The method provides information on the confidence level of the estimates, and the biomass estimator performances are illustrated for the Bacillus thuringiensis delta -endotoxins production process. |
| Author | di Sciascio, Fernando Amicarelli, Adriana N. |
| Author_xml | – sequence: 1 givenname: Fernando surname: di Sciascio fullname: di Sciascio, Fernando email: fernando@inaut.unsj.edu.ar, fernando.di.sciascio@gmail.com – sequence: 2 givenname: Adriana N. surname: Amicarelli fullname: Amicarelli, Adriana N. email: amicarelli@inaut.unsj.edu.ar |
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| Keywords | Bayesian regression Gaussian process Biotechnological process Bacillus thuringiensis Extended Kalman filter Biomass estimation |
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| Snippet | This paper proposes a biomass concentration estimator for a batch biotechnological process based on Bayesian regression with Gaussian process. On the basis of... |
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| SubjectTerms | Bacillus thuringiensis Bayesian regression Biomass estimation Biotechnological process Extended Kalman filter Gaussian process |
| Title | Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression |
| URI | https://dx.doi.org/10.1016/j.compchemeng.2008.05.015 https://search.proquest.com/docview/19901712 |
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